Test Optimal Combination
Kevin Lu
October 28, 2017
1. Source Search Cointegration Combinations
source("./Mean Reversion/RMR.007 Search Cointegration Combinations.R", echo = FALSE, print.eval = FALSE)## Parsed with column specification:
## cols(
## date_unix = col_integer(),
## date_time = col_datetime(format = ""),
## high = col_double(),
## low = col_double(),
## open = col_double(),
## close = col_double(),
## volume = col_double(),
## quote_volume = col_double(),
## weighted_average = col_double(),
## currency_pair = col_character(),
## period = col_integer()
## )## [1] "1: Testing for time resolution 900 from 2017-07-07 to 2017-08-23."
## [1] "2: Testing for time resolution 900 from 2017-01-29 to 2017-03-06."
## [1] "3: Testing for time resolution 7200 from 2017-08-10 to 2017-09-27."
## [1] "4: Testing for time resolution 300 from 2017-03-16 to 2017-04-11."
## [1] "5: Testing for time resolution 900 from 2017-06-03 to 2017-06-18."
## [1] "6: Testing for time resolution 900 from 2017-04-17 to 2017-06-01."
## [1] "7: Testing for time resolution 7200 from 2017-08-19 to 2017-10-03."
## [1] "8: Testing for time resolution 14400 from 2017-02-27 to 2017-03-12."
## [1] "9: Testing for time resolution 300 from 2017-05-12 to 2017-06-04."
## [1] "10: Testing for time resolution 86400 from 2017-02-08 to 2017-03-28."## Parsed with column specification:
## cols(
## df_stat = col_double(),
## crit_value_1pct = col_double(),
## crit_value_5pct = col_double(),
## crit_value_10pct = col_double(),
## half_life = col_double(),
## time_resolution = col_integer(),
## start_date = col_date(format = ""),
## end_date = col_date(format = ""),
## length = col_integer()
## )## # A tibble: 10 x 14
## df_stat_mean crit_value_1pct_mean crit_value_5pct_mean
## <dbl> <dbl> <dbl>
## 1 -2.705727 -3.43 -2.86
## 2 -2.205763 -3.43 -2.86
## 3 -2.522380 -3.43 -2.86
## 4 -3.358555 -3.43 -2.86
## 5 -1.919454 -3.43 -2.86
## 6 -2.964059 -3.43 -2.86
## 7 -2.594580 -3.43 -2.86
## 8 -2.211002 -3.51 -2.89
## 9 -3.661327 -3.43 -2.86
## 10 -2.529785 -3.58 -2.93
## # ... with 11 more variables: crit_value_10pct_mean <dbl>,
## # half_life_mean <dbl>, df_stat_median <dbl>,
## # crit_value_1pct_median <dbl>, crit_value_5pct_median <dbl>,
## # crit_value_10pct_median <dbl>, half_life_median <dbl>,
## # time_resolution <dbl>, start_date <date>, end_date <date>,
## # length <time>## Parsed with column specification:
## cols(
## df_stat_mean = col_double(),
## crit_value_1pct_mean = col_double(),
## crit_value_5pct_mean = col_double(),
## crit_value_10pct_mean = col_double(),
## half_life_mean = col_double(),
## df_stat_median = col_double(),
## crit_value_1pct_median = col_double(),
## crit_value_5pct_median = col_double(),
## crit_value_10pct_median = col_double(),
## half_life_median = col_double(),
## time_resolution = col_integer(),
## start_date = col_date(format = ""),
## end_date = col_date(format = ""),
## length = col_integer()
## )## # A tibble: 10 x 14
## df_stat_mean crit_value_1pct_mean crit_value_5pct_mean
## <dbl> <dbl> <dbl>
## 1 -2.705727 -3.43 -2.86
## 2 -2.205763 -3.43 -2.86
## 3 -2.522380 -3.43 -2.86
## 4 -3.358555 -3.43 -2.86
## 5 -1.919454 -3.43 -2.86
## 6 -2.964059 -3.43 -2.86
## 7 -2.594580 -3.43 -2.86
## 8 -2.211002 -3.51 -2.89
## 9 -3.661327 -3.43 -2.86
## 10 -2.529785 -3.58 -2.93
## # ... with 11 more variables: crit_value_10pct_mean <dbl>,
## # half_life_mean <dbl>, df_stat_median <dbl>,
## # crit_value_1pct_median <dbl>, crit_value_5pct_median <dbl>,
## # crit_value_10pct_median <dbl>, half_life_median <dbl>,
## # time_resolution <dbl>, start_date <date>, end_date <date>,
## # length <time>2. Prepare Data and Calculate Statistics
For each time resolution, prepare the pricing data and test for cointegration for all 98 coin pairs. The date of study is 2017-09-01 to 2017-09-30. This period has exhibited strong mean reversion.
pricing_data_300 <- prepare_data(time_resolution = 300, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_900 <- prepare_data(time_resolution = 900, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_1800 <- prepare_data(time_resolution = 1800, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_7200 <- prepare_data(time_resolution = 7200, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_14400 <- prepare_data(time_resolution = 14400, start_date = "2017-09-01", end_date = "2017-09-30")
pricing_data_86400 <- prepare_data(time_resolution = 86400, start_date = "2017-09-01", end_date = "2017-09-30")
coin_pairs_300 <- calculate_statistics(pricing_data = pricing_data_300, coin_pairs = create_coins())
coin_pairs_900 <- calculate_statistics(pricing_data = pricing_data_900, coin_pairs = create_coins())
coin_pairs_1800 <- calculate_statistics(pricing_data = pricing_data_1800, coin_pairs = create_coins())
coin_pairs_7200 <- calculate_statistics(pricing_data = pricing_data_7200, coin_pairs = create_coins())
coin_pairs_14400 <- calculate_statistics(pricing_data = pricing_data_14400, coin_pairs = create_coins())
coin_pairs_86400 <- calculate_statistics(pricing_data = pricing_data_86400, coin_pairs = create_coins())3. Plot Time Resolution 300
For each time resolution, plot the top 10 coins ranked by the ADF test statistic.
for (i in 1:10) {
coin_y <- coin_pairs_300[["coin_y"]][i]
coin_x <- coin_pairs_300[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[[coin_y]],
coin_x = pricing_data_300[[coin_x]])
}## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001342 0.000000035 0.000001233 0.000006823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000342914 0.0000001786 192.0 <0.0000000000000002 ***
## coin_x 0.0017825242 0.0000119346 149.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001836 on 8351 degrees of freedom
## Multiple R-squared: 0.7276, Adjusted R-squared: 0.7276
## F-statistic: 2.231e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8470 -0.5730 -0.1290 0.4191 4.2581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76564090 0.10152549 -96.19 <0.0000000000000002 ***
## coin_x 0.00741490 0.00002486 298.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8201 on 8351 degrees of freedom
## Multiple R-squared: 0.9142, Adjusted R-squared: 0.9142
## F-statistic: 8.894e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_BTC and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -535.99 -52.48 12.85 73.27 231.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1553.1077 8.5097 182.5 <0.0000000000000002 ***
## coin_x 123.2873 0.4134 298.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105.7 on 8351 degrees of freedom
## Multiple R-squared: 0.9142, Adjusted R-squared: 0.9142
## F-statistic: 8.894e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.7857 -1.6548 -0.3121 1.0235 24.4679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.96177 0.23045 156.1 <0.0000000000000002 ***
## coin_x 1.13133 0.00371 304.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.976 on 8351 degrees of freedom
## Multiple R-squared: 0.9176, Adjusted R-squared: 0.9176
## F-statistic: 9.297e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.4296 -1.3426 -0.0774 1.4087 10.5354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.14007 0.28163 -85.72 <0.0000000000000002 ***
## coin_x 0.81106 0.00266 304.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.366 on 8351 degrees of freedom
## Multiple R-squared: 0.9176, Adjusted R-squared: 0.9176
## F-statistic: 9.297e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0293 -0.5929 -0.1819 0.3923 4.1597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.55925 0.07238 21.54 <0.0000000000000002 ***
## coin_x 75.85317 0.28868 262.76 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9195 on 8351 degrees of freedom
## Multiple R-squared: 0.8921, Adjusted R-squared: 0.8921
## F-statistic: 6.904e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XEM and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036125 -0.007592 0.002155 0.006693 0.040358
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.074905407 0.000590279 126.9 <0.0000000000000002 ***
## coin_x 0.002842819 0.000009504 299.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01018 on 8351 degrees of freedom
## Multiple R-squared: 0.9146, Adjusted R-squared: 0.9146
## F-statistic: 8.947e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XEM and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050135 -0.004313 0.001641 0.007033 0.030006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00845403 0.00092134 9.176 <0.0000000000000002 ***
## coin_x 0.01176083 0.00004476 262.759 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01145 on 8351 degrees of freedom
## Multiple R-squared: 0.8921, Adjusted R-squared: 0.8921
## F-statistic: 6.904e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00261762 -0.00071480 -0.00007712 0.00068928 0.00282866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0099463 0.0001665 -59.75 <0.0000000000000002 ***
## coin_x 408.1931633 2.7329907 149.36 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0008788 on 8351 degrees of freedom
## Multiple R-squared: 0.7276, Adjusted R-squared: 0.7276
## F-statistic: 2.231e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00046389 -0.00013994 -0.00004017 0.00010558 0.00093721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00318938 0.00001868 170.71 <0.0000000000000002 ***
## coin_x 0.12142256 0.00124820 97.28 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001921 on 8351 degrees of freedom
## Multiple R-squared: 0.5312, Adjusted R-squared: 0.5312
## F-statistic: 9463 on 1 and 8351 DF, p-value: < 0.00000000000000022
4. Plot Time Resolution 900
for (i in 1:10) {
coin_y <- coin_pairs_900[["coin_y"]][i]
coin_x <- coin_pairs_900[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[[coin_y]],
coin_x = pricing_data_900[[coin_x]])
}## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001345 0.000000029 0.000001240 0.000006653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000034272 0.000000309 110.92 <0.0000000000000002 ***
## coin_x 0.001783861 0.000020643 86.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001835 on 2783 degrees of freedom
## Multiple R-squared: 0.7285, Adjusted R-squared: 0.7284
## F-statistic: 7467 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7046 -1.6898 -0.3227 1.0009 24.1214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.967359 0.398511 90.25 <0.0000000000000002 ***
## coin_x 1.131332 0.006417 176.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.971 on 2783 degrees of freedom
## Multiple R-squared: 0.9178, Adjusted R-squared: 0.9178
## F-statistic: 3.108e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1291 -1.3225 -0.0763 1.4338 8.8500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.167337 0.487184 -49.61 <0.0000000000000002 ***
## coin_x 0.811269 0.004602 176.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.362 on 2783 degrees of freedom
## Multiple R-squared: 0.9178, Adjusted R-squared: 0.9178
## F-statistic: 3.108e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00239641 -0.00071868 -0.00008315 0.00069136 0.00283113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0099584 0.0002878 -34.60 <0.0000000000000002 ***
## coin_x 408.3809852 4.7259022 86.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000878 on 2783 degrees of freedom
## Multiple R-squared: 0.7285, Adjusted R-squared: 0.7284
## F-statistic: 7467 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XEM and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036125 -0.007577 0.002205 0.006700 0.039886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07487695 0.00102019 73.39 <0.0000000000000002 ***
## coin_x 0.00284344 0.00001643 173.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01017 on 2783 degrees of freedom
## Multiple R-squared: 0.915, Adjusted R-squared: 0.915
## F-statistic: 2.996e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7288 -0.5774 -0.1290 0.4189 4.2573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.73062649 0.17668477 -55.07 <0.0000000000000002 ***
## coin_x 0.00740622 0.00004327 171.16 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 2783 degrees of freedom
## Multiple R-squared: 0.9132, Adjusted R-squared: 0.9132
## F-statistic: 2.929e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_BTC and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -536.07 -53.24 12.89 73.00 215.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1552.7191 14.8285 104.7 <0.0000000000000002 ***
## coin_x 123.3072 0.7204 171.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 106.4 on 2783 degrees of freedom
## Multiple R-squared: 0.9132, Adjusted R-squared: 0.9132
## F-statistic: 2.929e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00046354 -0.00013934 -0.00004032 0.00010333 0.00093508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00318550 0.00003238 98.39 <0.0000000000000002 ***
## coin_x 0.12167755 0.00216326 56.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001923 on 2783 degrees of freedom
## Multiple R-squared: 0.532, Adjusted R-squared: 0.5318
## F-statistic: 3164 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.6060 -2.7107 -0.7245 2.8581 10.3138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.9112 0.4661 -40.57 <0.0000000000000002 ***
## coin_x 321.7923 1.8592 173.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.42 on 2783 degrees of freedom
## Multiple R-squared: 0.915, Adjusted R-squared: 0.915
## F-statistic: 2.996e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9671 -0.5868 -0.1877 0.3978 4.0641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5685 0.1255 12.5 <0.0000000000000002 ***
## coin_x 75.8143 0.5005 151.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9206 on 2783 degrees of freedom
## Multiple R-squared: 0.8918, Adjusted R-squared: 0.8918
## F-statistic: 2.294e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
5. Plot Time Resolution 1800
for (i in 1:10) {
coin_y <- coin_pairs_1800[["coin_y"]][i]
coin_x <- coin_pairs_1800[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[[coin_y]],
coin_x = pricing_data_1800[[coin_x]])
}## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6872 -1.6639 -0.3182 0.9912 24.1493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.992859 0.560945 64.17 <0.0000000000000002 ***
## coin_x 1.130652 0.009035 125.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.957 on 1391 degrees of freedom
## Multiple R-squared: 0.9184, Adjusted R-squared: 0.9184
## F-statistic: 1.566e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1857 -1.3133 -0.0869 1.3680 8.8470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.263151 0.686976 -35.32 <0.0000000000000002 ***
## coin_x 0.812293 0.006491 125.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.354 on 1391 degrees of freedom
## Multiple R-squared: 0.9184, Adjusted R-squared: 0.9184
## F-statistic: 1.566e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075557 -0.0000013567 -0.0000000041 0.0000012434 0.0000065578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000343156 0.0000004359 78.73 <0.0000000000000002 ***
## coin_x 0.0017808785 0.0000291278 61.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001833 on 1391 degrees of freedom
## Multiple R-squared: 0.7288, Adjusted R-squared: 0.7286
## F-statistic: 3738 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00046434 -0.00013920 -0.00003941 0.00010537 0.00093452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00318808 0.00004553 70.02 <0.0000000000000002 ***
## coin_x 0.12154940 0.00304290 39.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001915 on 1391 degrees of freedom
## Multiple R-squared: 0.5343, Adjusted R-squared: 0.5339
## F-statistic: 1596 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7184 -0.5726 -0.1294 0.4224 4.2610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76197830 0.24983070 -39.07 <0.0000000000000002 ***
## coin_x 0.00741325 0.00006119 121.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 1391 degrees of freedom
## Multiple R-squared: 0.9134, Adjusted R-squared: 0.9134
## F-statistic: 1.468e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_BTC and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -536.07 -52.98 12.34 72.77 213.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1554.891 20.929 74.3 <0.0000000000000002 ***
## coin_x 123.216 1.017 121.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 106.3 on 1391 degrees of freedom
## Multiple R-squared: 0.9134, Adjusted R-squared: 0.9134
## F-statistic: 1.468e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00240240 -0.00071702 -0.00008127 0.00069541 0.00283937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0100109 0.0004076 -24.56 <0.0000000000000002 ***
## coin_x 409.2378593 6.6934448 61.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0008786 on 1391 degrees of freedom
## Multiple R-squared: 0.7288, Adjusted R-squared: 0.7286
## F-statistic: 3738 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XEM and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.036170 -0.007597 0.002155 0.006782 0.039936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07506118 0.00143767 52.21 <0.0000000000000002 ***
## coin_x 0.00284034 0.00002316 122.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01014 on 1391 degrees of freedom
## Multiple R-squared: 0.9154, Adjusted R-squared: 0.9153
## F-statistic: 1.504e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9281 -0.6116 -0.1834 0.3915 4.0667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5433 0.1779 8.677 <0.0000000000000002 ***
## coin_x 75.9092 0.7095 106.983 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9225 on 1391 degrees of freedom
## Multiple R-squared: 0.8916, Adjusted R-squared: 0.8916
## F-statistic: 1.145e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XEM and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048842 -0.004445 0.001537 0.007044 0.028590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0087709 0.0022593 3.882 0.000108 ***
## coin_x 0.0117461 0.0001098 106.983 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01148 on 1391 degrees of freedom
## Multiple R-squared: 0.8916, Adjusted R-squared: 0.8916
## F-statistic: 1.145e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
6. Plot Time Resolution 7200
for (i in 1:10) {
coin_y <- coin_pairs_7200[["coin_y"]][i]
coin_x <- coin_pairs_7200[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[[coin_y]],
coin_x = pricing_data_7200[[coin_x]])
}## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6386 -1.6524 -0.2642 0.9648 19.3184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.99738 1.08940 33.04 <0.0000000000000002 ***
## coin_x 1.12981 0.01756 64.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.855 on 347 degrees of freedom
## Multiple R-squared: 0.9227, Adjusted R-squared: 0.9224
## F-statistic: 4140 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075117 -0.0000013231 -0.0000000155 0.0000012043 0.0000060980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000341619 0.0000008857 38.57 <0.0000000000000002 ***
## coin_x 0.0017887492 0.0000592241 30.20 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001867 on 347 degrees of freedom
## Multiple R-squared: 0.7244, Adjusted R-squared: 0.7236
## F-statistic: 912.2 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.3973 -1.2895 0.0341 1.3039 8.8491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.68618 1.34197 -18.39 <0.0000000000000002 ***
## coin_x 0.81665 0.01269 64.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.278 on 347 degrees of freedom
## Multiple R-squared: 0.9227, Adjusted R-squared: 0.9224
## F-statistic: 4140 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0038536 -0.0016410 -0.0004824 0.0009370 0.0104390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.057670 0.001762 32.72 < 0.0000000000000002 ***
## coin_x 0.182259 0.021670 8.41 0.00000000000000108 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002588 on 347 degrees of freedom
## Multiple R-squared: 0.1693, Adjusted R-squared: 0.1669
## F-statistic: 70.74 on 1 and 347 DF, p-value: 0.000000000000001076
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5779 -0.6011 -0.1128 0.4236 2.6516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.7521113 0.4882912 -19.97 <0.0000000000000002 ***
## coin_x 0.0074100 0.0001196 61.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8108 on 347 degrees of freedom
## Multiple R-squared: 0.9171, Adjusted R-squared: 0.9168
## F-statistic: 3838 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XEM and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035870 -0.007766 0.002150 0.007063 0.035942
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07418121 0.00289484 25.62 <0.0000000000000002 ***
## coin_x 0.00285327 0.00004666 61.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01024 on 347 degrees of freedom
## Multiple R-squared: 0.9151, Adjusted R-squared: 0.9148
## F-statistic: 3739 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00062429 -0.00015017 -0.00003047 0.00010655 0.00088295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0068374 0.0001621 42.19 <0.0000000000000002 ***
## coin_x -0.0227435 0.0019925 -11.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0002379 on 347 degrees of freedom
## Multiple R-squared: 0.273, Adjusted R-squared: 0.2709
## F-statistic: 130.3 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_BTC and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -341.11 -55.55 11.93 74.44 200.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1544.101 41.095 37.57 <0.0000000000000002 ***
## coin_x 123.762 1.998 61.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 104.8 on 347 degrees of freedom
## Multiple R-squared: 0.9171, Adjusted R-squared: 0.9168
## F-statistic: 3838 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00043187 -0.00013841 -0.00003465 0.00010510 0.00059920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00319881 0.00009016 35.48 <0.0000000000000002 ***
## coin_x 0.12076320 0.00602842 20.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00019 on 347 degrees of freedom
## Multiple R-squared: 0.5363, Adjusted R-squared: 0.5349
## F-statistic: 401.3 on 1 and 347 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_LTC and BTC_XEM."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00211385 -0.00073957 -0.00009388 0.00070228 0.00278791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0097405 0.0008159 -11.94 <0.0000000000000002 ***
## coin_x 404.9946207 13.4090534 30.20 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0008883 on 347 degrees of freedom
## Multiple R-squared: 0.7244, Adjusted R-squared: 0.7236
## F-statistic: 912.2 on 1 and 347 DF, p-value: < 0.00000000000000022
7. Plot Time Resolution 14400
for (i in 1:10) {
coin_y <- coin_pairs_14400[["coin_y"]][i]
coin_x <- coin_pairs_14400[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[[coin_y]],
coin_x = pricing_data_14400[[coin_x]])
}## [1] "Generating plots for USDT_XMR and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5723 -1.8244 -0.2084 0.9656 19.3276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.71162 1.51967 23.50 <0.0000000000000002 ***
## coin_x 1.13329 0.02451 46.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.818 on 173 degrees of freedom
## Multiple R-squared: 0.9251, Adjusted R-squared: 0.9247
## F-statistic: 2138 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_LTC and USDT_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.4427 -1.3155 0.0483 1.2022 8.7895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.59567 1.86460 -13.19 <0.0000000000000002 ***
## coin_x 0.81633 0.01765 46.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.241 on 173 degrees of freedom
## Multiple R-squared: 0.9251, Adjusted R-squared: 0.9247
## F-statistic: 2138 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0038341 -0.0016368 -0.0004610 0.0009655 0.0099446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.057726 0.002486 23.22 < 0.0000000000000002 ***
## coin_x 0.181335 0.030577 5.93 0.000000016 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002568 on 173 degrees of freedom
## Multiple R-squared: 0.1689, Adjusted R-squared: 0.1641
## F-statistic: 35.17 on 1 and 173 DF, p-value: 0.00000001602
## [1] "Generating plots for BTC_XEM and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000074526 -0.0000012903 0.0000000175 0.0000011949 0.0000057830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000033667 0.000001276 26.39 <0.0000000000000002 ***
## coin_x 0.001819995 0.000085378 21.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001909 on 173 degrees of freedom
## Multiple R-squared: 0.7243, Adjusted R-squared: 0.7227
## F-statistic: 454.4 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_XEM and USDT_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.035518 -0.006721 0.001962 0.007202 0.031888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07280959 0.00410622 17.73 <0.0000000000000002 ***
## coin_x 0.00287598 0.00006622 43.43 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01032 on 173 degrees of freedom
## Multiple R-squared: 0.916, Adjusted R-squared: 0.9155
## F-statistic: 1886 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_REP and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00061706 -0.00014454 -0.00002468 0.00009220 0.00081652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0067990 0.0002285 29.75 < 0.0000000000000002 ***
## coin_x -0.0223197 0.0028111 -7.94 0.000000000000245 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0002361 on 173 degrees of freedom
## Multiple R-squared: 0.2671, Adjusted R-squared: 0.2628
## F-statistic: 63.04 on 1 and 173 DF, p-value: 0.0000000000002455
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00043173 -0.00013070 -0.00002933 0.00010251 0.00056316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0032174 0.0001248 25.77 <0.0000000000000002 ***
## coin_x 0.1194186 0.0083552 14.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001868 on 173 degrees of freedom
## Multiple R-squared: 0.5415, Adjusted R-squared: 0.5388
## F-statistic: 204.3 on 1 and 173 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0047124 -0.0021563 -0.0002285 0.0013427 0.0098794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.070450 0.001877 37.531 <0.0000000000000002 ***
## coin_x 0.132759 0.125633 1.057 0.292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002808 on 173 degrees of freedom
## Multiple R-squared: 0.006413, Adjusted R-squared: 0.00067
## F-statistic: 1.117 on 1 and 173 DF, p-value: 0.2921
## [1] "Generating plots for BTC_ETH and BTC_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0049713 -0.0021473 -0.0000822 0.0013244 0.0097292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.069648 0.003246 21.459 <0.0000000000000002 ***
## coin_x 0.107997 0.126095 0.856 0.393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002811 on 173 degrees of freedom
## Multiple R-squared: 0.004222, Adjusted R-squared: -0.001534
## F-statistic: 0.7335 on 1 and 173 DF, p-value: 0.3929
## [1] "Generating plots for BTC_ETH and BTC_ZEC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0043253 -0.0019186 -0.0003149 0.0012466 0.0094418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.066200 0.001445 45.810 < 0.0000000000000002 ***
## coin_x 0.113922 0.026202 4.348 0.0000234 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002675 on 173 degrees of freedom
## Multiple R-squared: 0.09851, Adjusted R-squared: 0.09329
## F-statistic: 18.9 on 1 and 173 DF, p-value: 0.00002342
8. Plot Time Resolution 86400
for (i in 1:10) {
coin_y <- coin_pairs_86400[["coin_y"]][i]
coin_x <- coin_pairs_86400[["coin_x"]][i]
print(str_c("Generating plots for ", coin_y, " and ", coin_x, "."))
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[[coin_y]],
coin_x = pricing_data_86400[[coin_x]])
}## [1] "Generating plots for BTC_ETH and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.003306 -0.001553 -0.000233 0.001127 0.004917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.058642 0.005179 11.323 0.00000000000927 ***
## coin_x 0.167030 0.063661 2.624 0.0141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002105 on 27 degrees of freedom
## Multiple R-squared: 0.2032, Adjusted R-squared: 0.1737
## F-statistic: 6.884 on 1 and 27 DF, p-value: 0.01413
## [1] "Generating plots for BTC_REP and BTC_DASH."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00028213 -0.00010269 0.00002135 0.00006380 0.00035581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0068313 0.0004061 16.824 0.000000000000000776 ***
## coin_x -0.0229002 0.0049910 -4.588 0.000092072428839062 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001651 on 27 degrees of freedom
## Multiple R-squared: 0.4381, Adjusted R-squared: 0.4173
## F-statistic: 21.05 on 1 and 27 DF, p-value: 0.00009207
## [1] "Generating plots for BTC_ETH and BTC_XMR."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0032331 -0.0018057 -0.0002109 0.0012201 0.0046517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075642 0.007091 10.667 0.0000000000349 ***
## coin_x -0.135217 0.277424 -0.487 0.63
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002348 on 27 degrees of freedom
## Multiple R-squared: 0.008722, Adjusted R-squared: -0.02799
## F-statistic: 0.2376 on 1 and 27 DF, p-value: 0.6299
## [1] "Generating plots for USDT_BTC and USDT_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -226.243 -45.628 0.156 62.820 208.665
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1254.135 152.999 8.197 0.0000000084 ***
## coin_x 138.297 7.512 18.409 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 97.35 on 27 degrees of freedom
## Multiple R-squared: 0.9262, Adjusted R-squared: 0.9235
## F-statistic: 338.9 on 1 and 27 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for USDT_REP and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4944 -0.3292 -0.1622 0.4061 1.5361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.9069160 1.4791046 -4.67 0.000074 ***
## coin_x 0.0066972 0.0003638 18.41 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6775 on 27 degrees of freedom
## Multiple R-squared: 0.9262, Adjusted R-squared: 0.9235
## F-statistic: 338.9 on 1 and 27 DF, p-value: < 0.00000000000000022
## [1] "Generating plots for BTC_ETH and BTC_ZEC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0033457 -0.0016413 -0.0004947 0.0012814 0.0046917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.067954 0.003514 19.336 <0.0000000000000002 ***
## coin_x 0.077761 0.064010 1.215 0.235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002297 on 27 degrees of freedom
## Multiple R-squared: 0.05183, Adjusted R-squared: 0.01671
## F-statistic: 1.476 on 1 and 27 DF, p-value: 0.2349
## [1] "Generating plots for BTC_ETH and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0032889 -0.0018485 0.0000349 0.0011442 0.0048716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.071685 0.003874 18.505 <0.0000000000000002 ***
## coin_x 0.034509 0.261678 0.132 0.896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002358 on 27 degrees of freedom
## Multiple R-squared: 0.0006437, Adjusted R-squared: -0.03637
## F-statistic: 0.01739 on 1 and 27 DF, p-value: 0.8961
## [1] "Generating plots for USDT_ETH and USDT_BTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.032 -6.920 -1.184 4.843 20.412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.746489 20.421114 -0.918 0.367
## coin_x 0.076925 0.005023 15.315 0.00000000000000777 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.354 on 27 degrees of freedom
## Multiple R-squared: 0.8968, Adjusted R-squared: 0.8929
## F-statistic: 234.6 on 1 and 27 DF, p-value: 0.00000000000000777
## [1] "Generating plots for BTC_REP and BTC_LTC."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00022344 -0.00010018 -0.00001669 0.00007055 0.00048623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0035797 0.0002408 14.863 0.0000000000000161 ***
## coin_x 0.0947604 0.0162691 5.825 0.0000033630484185 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001466 on 27 degrees of freedom
## Multiple R-squared: 0.5568, Adjusted R-squared: 0.5404
## F-statistic: 33.93 on 1 and 27 DF, p-value: 0.000003363
## [1] "Generating plots for BTC_ETH and BTC_REP."
##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0031017 -0.0019468 -0.0002738 0.0012263 0.0049217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06703 0.01021 6.563 0.000000487 ***
## coin_x 1.03833 2.05160 0.506 0.617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002348 on 27 degrees of freedom
## Multiple R-squared: 0.009398, Adjusted R-squared: -0.02729
## F-statistic: 0.2561 on 1 and 27 DF, p-value: 0.6169
9. BTC_XEM and BTC_LTC Coin Pair
An examination of BTC_XEM and BTC_LTC across time resolutions.
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[["BTC_XEM"]],
coin_x = pricing_data_300[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001342 0.000000035 0.000001233 0.000006823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000342914 0.0000001786 192.0 <0.0000000000000002 ***
## coin_x 0.0017825242 0.0000119346 149.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001836 on 8351 degrees of freedom
## Multiple R-squared: 0.7276, Adjusted R-squared: 0.7276
## F-statistic: 2.231e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[["BTC_XEM"]],
coin_x = pricing_data_900[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000007554 -0.000001345 0.000000029 0.000001240 0.000006653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000034272 0.000000309 110.92 <0.0000000000000002 ***
## coin_x 0.001783861 0.000020643 86.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001835 on 2783 degrees of freedom
## Multiple R-squared: 0.7285, Adjusted R-squared: 0.7284
## F-statistic: 7467 on 1 and 2783 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[["BTC_XEM"]],
coin_x = pricing_data_1800[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075557 -0.0000013567 -0.0000000041 0.0000012434 0.0000065578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000343156 0.0000004359 78.73 <0.0000000000000002 ***
## coin_x 0.0017808785 0.0000291278 61.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001833 on 1391 degrees of freedom
## Multiple R-squared: 0.7288, Adjusted R-squared: 0.7286
## F-statistic: 3738 on 1 and 1391 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[["BTC_XEM"]],
coin_x = pricing_data_7200[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000075117 -0.0000013231 -0.0000000155 0.0000012043 0.0000060980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0000341619 0.0000008857 38.57 <0.0000000000000002 ***
## coin_x 0.0017887492 0.0000592241 30.20 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001867 on 347 degrees of freedom
## Multiple R-squared: 0.7244, Adjusted R-squared: 0.7236
## F-statistic: 912.2 on 1 and 347 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[["BTC_XEM"]],
coin_x = pricing_data_14400[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000074526 -0.0000012903 0.0000000175 0.0000011949 0.0000057830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000033667 0.000001276 26.39 <0.0000000000000002 ***
## coin_x 0.001819995 0.000085378 21.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000001909 on 173 degrees of freedom
## Multiple R-squared: 0.7243, Adjusted R-squared: 0.7227
## F-statistic: 454.4 on 1 and 173 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[["BTC_XEM"]],
coin_x = pricing_data_86400[["BTC_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000072622 -0.0000010911 0.0000001578 0.0000013416 0.0000027461
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000034025 0.000003437 9.899 0.000000000176 ***
## coin_x 0.001780634 0.000232186 7.669 0.000000030065 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000002092 on 27 degrees of freedom
## Multiple R-squared: 0.6854, Adjusted R-squared: 0.6737
## F-statistic: 58.81 on 1 and 27 DF, p-value: 0.00000003007
9. USDT_REP and USDT_BTC Coin Pair
An examination of USDT_REP and USDT_BTC across time resolutions.
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[["USDT_REP"]],
coin_x = pricing_data_300[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8470 -0.5730 -0.1290 0.4191 4.2581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76564090 0.10152549 -96.19 <0.0000000000000002 ***
## coin_x 0.00741490 0.00002486 298.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8201 on 8351 degrees of freedom
## Multiple R-squared: 0.9142, Adjusted R-squared: 0.9142
## F-statistic: 8.894e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[["USDT_REP"]],
coin_x = pricing_data_900[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7288 -0.5774 -0.1290 0.4189 4.2573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.73062649 0.17668477 -55.07 <0.0000000000000002 ***
## coin_x 0.00740622 0.00004327 171.16 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 2783 degrees of freedom
## Multiple R-squared: 0.9132, Adjusted R-squared: 0.9132
## F-statistic: 2.929e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[["USDT_REP"]],
coin_x = pricing_data_1800[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7184 -0.5726 -0.1294 0.4224 4.2610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.76197830 0.24983070 -39.07 <0.0000000000000002 ***
## coin_x 0.00741325 0.00006119 121.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 1391 degrees of freedom
## Multiple R-squared: 0.9134, Adjusted R-squared: 0.9134
## F-statistic: 1.468e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[["USDT_REP"]],
coin_x = pricing_data_7200[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5779 -0.6011 -0.1128 0.4236 2.6516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.7521113 0.4882912 -19.97 <0.0000000000000002 ***
## coin_x 0.0074100 0.0001196 61.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8108 on 347 degrees of freedom
## Multiple R-squared: 0.9171, Adjusted R-squared: 0.9168
## F-statistic: 3838 on 1 and 347 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[["USDT_REP"]],
coin_x = pricing_data_14400[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.55987 -0.59252 -0.09366 0.44002 2.14749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.6484195 0.6844643 -14.10 <0.0000000000000002 ***
## coin_x 0.0073797 0.0001676 44.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8098 on 173 degrees of freedom
## Multiple R-squared: 0.9181, Adjusted R-squared: 0.9176
## F-statistic: 1939 on 1 and 173 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[["USDT_REP"]],
coin_x = pricing_data_86400[["USDT_BTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4944 -0.3292 -0.1622 0.4061 1.5361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.9069160 1.4791046 -4.67 0.000074 ***
## coin_x 0.0066972 0.0003638 18.41 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6775 on 27 degrees of freedom
## Multiple R-squared: 0.9262, Adjusted R-squared: 0.9235
## F-statistic: 338.9 on 1 and 27 DF, p-value: < 0.00000000000000022
10. USDT_XMR and USDT_LTC Coin Pair
An examination of USDT_XMR and USDT_LTC across time resolutions.
plot_coins(df = pricing_data_300,
coin_y = pricing_data_300[["USDT_XMR"]],
coin_x = pricing_data_300[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.7857 -1.6548 -0.3121 1.0235 24.4679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.96177 0.23045 156.1 <0.0000000000000002 ***
## coin_x 1.13133 0.00371 304.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.976 on 8351 degrees of freedom
## Multiple R-squared: 0.9176, Adjusted R-squared: 0.9176
## F-statistic: 9.297e+04 on 1 and 8351 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_900,
coin_y = pricing_data_900[["USDT_XMR"]],
coin_x = pricing_data_900[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7046 -1.6898 -0.3227 1.0009 24.1214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.967359 0.398511 90.25 <0.0000000000000002 ***
## coin_x 1.131332 0.006417 176.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.971 on 2783 degrees of freedom
## Multiple R-squared: 0.9178, Adjusted R-squared: 0.9178
## F-statistic: 3.108e+04 on 1 and 2783 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_1800,
coin_y = pricing_data_1800[["USDT_XMR"]],
coin_x = pricing_data_1800[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6872 -1.6639 -0.3182 0.9912 24.1493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.992859 0.560945 64.17 <0.0000000000000002 ***
## coin_x 1.130652 0.009035 125.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.957 on 1391 degrees of freedom
## Multiple R-squared: 0.9184, Adjusted R-squared: 0.9184
## F-statistic: 1.566e+04 on 1 and 1391 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_7200,
coin_y = pricing_data_7200[["USDT_XMR"]],
coin_x = pricing_data_7200[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6386 -1.6524 -0.2642 0.9648 19.3184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.99738 1.08940 33.04 <0.0000000000000002 ***
## coin_x 1.12981 0.01756 64.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.855 on 347 degrees of freedom
## Multiple R-squared: 0.9227, Adjusted R-squared: 0.9224
## F-statistic: 4140 on 1 and 347 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_14400,
coin_y = pricing_data_14400[["USDT_XMR"]],
coin_x = pricing_data_14400[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5723 -1.8244 -0.2084 0.9656 19.3276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.71162 1.51967 23.50 <0.0000000000000002 ***
## coin_x 1.13329 0.02451 46.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.818 on 173 degrees of freedom
## Multiple R-squared: 0.9251, Adjusted R-squared: 0.9247
## F-statistic: 2138 on 1 and 173 DF, p-value: < 0.00000000000000022
plot_coins(df = pricing_data_86400,
coin_y = pricing_data_86400[["USDT_XMR"]],
coin_x = pricing_data_86400[["USDT_LTC"]])##
## Call:
## lm(formula = coin_y ~ coin_x, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.410 -1.666 0.186 1.522 5.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.92702 2.45102 16.29 0.00000000000000172 ***
## coin_x 1.05803 0.04013 26.36 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.415 on 27 degrees of freedom
## Multiple R-squared: 0.9626, Adjusted R-squared: 0.9612
## F-statistic: 695.1 on 1 and 27 DF, p-value: < 0.00000000000000022
